页岩气因其储层条件复杂、多尺度孔隙结构，产量随时间变化呈现明显的非线性特征，传统的产量预测方法依赖于地质和工程数据的统计分析，很难适应地质条件的复杂性而无法达到高准确性。提出了将超双曲递减模型与具有时间属性的复合函数结合的方法，并使用改进的自适应粒子群优化算法（adaptive-particle swarm optimization, A-PSO）来寻找最优模型参数，建立了复合时间超双曲递减模型。研究结果表明：采用A-PSO优化算法能够根据产量数据的复杂性和数据的变化自动调整参数和模型结构，能更快更准地找到最优参数组合，提高预测精度；产量在时间上的波动大，常规递减模型难以反映其特征，复合时间递减模型灵活性强，能够考虑油气藏的复杂性和多变性，更准确地描述页岩气井在不同阶段的产量变化，提供更高的拟合精度，使得产量预测更接近实际值。

Due to the complex reservoir conditions and multi-scale pore structure of shale gas, the production shows significant nonlinear characteristics over time. Traditional production prediction methods, which rely on statistical analysis of geological and engineering data, find it difficult to adapt to the complexity of geological conditions and thus cannot achieve high accuracy. A method that combines the hyperbolic decline model with a composite function having time attributes was proposed. The improved A-PSO (adaptive particle swarm optimization algorithm) was used to find the optimal model parameters, establishing a composite time hyperbolic decline model. The research results show as follows. The A-PSO optimization algorithm can automatically adjust parameters and model structure according to the complexity of production data and data changes, finding the optimal parameter combination more quickly and accurately, thereby improving prediction accuracy. The production fluctuates greatly over time, making it difficult for conventional decline models to reflect its characteristics. The composite time decline model, with its strong flexibility, can consider the complexity and variability of oil and gas reservoirs, more accurately describe the production changes of shale gas wells at different stages, and provide higher fitting accuracy, making the production prediction closer to the actual value.